Analysis of CDSFNet and the cascade structure. This section first analyzes the effectiveness of proposed CDSFNet when increasing the number of candidate scales. To measure explicitly the matchability of features extracted by CDSFNet, we remove the cost-volume regularization step in the MVS architecture and then regress the depth directly from the aggregated 3D cost volume. Table REF  presents the results when increasing the number candidate scales \(N_c\)  in each CDSConv layer. When \(N_c\)  is equal to 1, CDSFNet is considered as Feature Pyramid Network (FPN), which is applied to most of the state-of-the-art learning-based methods [1]}, [2]}. As shown in Table REF , CDSFNet achieved better performance when using more candidate scales. However, it suffered from heavy computation. Therefore, we chose the design with two or three scales for each CDSConv (CDSFNet architecture as shown in Fig. REF ), to guarantee both the performance of reconstruction and computational efficiency.
